QSymbolic LLC

frank@qsymbolic.com

Author: Frank Cunnane

  • 🧠 The ROOM Annealer: Deterministic Collapse Meets Optimization

    When most people hear “annealing,” they think of quantum or simulated annealing — algorithms that wander probabilistically through energy landscapes, slowly cooling toward a global minimum. But what if you could anneal deterministically, in hardware, in a single clock domain?

    That’s the concept behind the ROOM Annealer, a hardware optimization engine built on the same Read-Only-Once Memory (ROOM) primitive that powers our post-algebraic cryptography research at QSymbolic.

    ⚙️ Collapse as a Search Primitive

    At the core of ROOM is a simple but radical idea:

    A memory cell that delivers its true value only once, then collapses into entropy.

    Each collapse cycle yields an irreversible, randomized outcome that can be fed into logic networks or constraint fabrics. When scaled across thousands of cells, these correlated collapses behave like a hardware sampler exploring a high-dimensional energy surface.

    Instead of using thermal or quantum randomness, ROOM uses CMOS determinism — clocked collapse events that unfold according to metadata constraints (basis, phase, parity). This means the system can anneal, sample, or optimize while remaining fully synchronous and predictable in timing.

    🔁 From Entropy to Optimization

    The ROOM Annealer treats its collapse network as an Ising-like lattice, where each cell’s output influences its neighbors through parity constraints. During an anneal pass:

    1. Initialization: a seed entropy source pre-loads the collapse states.
    2. Collapse: each cell releases its one-time value, feeding neighbors.
    3. Update: correlation logic adjusts local states toward lower global energy.
    4. Re-arm: the bank resets with obfuscated entropy for the next sweep.

    Within a few cycles, the system converges toward minimal-energy configurations — solving problems like MAX-CUT, portfolio optimization, or constraint satisfaction.

    Because it’s digital, timing-deterministic, and CMOS-native, ROOM achieves the speed of logic with the dynamics of annealing.

    🧩 What Makes It Different

    ConceptQuantum AnnealingSimulated AnnealingROOM Annealing
    MediumSuperconducting qubitsSoftware RNGCMOS memory cells
    Noise SourceQuantum tunnelingThermal randomnessDeterministic collapse entropy
    ControlAnalog bias currentsTemperature scheduleClock-driven metadata gating
    SpeedMicrosecondsMilliseconds–secondsNanoseconds–microseconds
    PrecisionProbabilisticProbabilisticReproducible deterministic

    In practice, ROOM sits between classical and quantum hardware — a “post-algebraic” layer capable of symbolic superposition without coherence or decoherence problems.

    🔐 Why It Matters

    The same mechanics that make ROOM ideal for one-time keys and secure hardware entropy also make it an ultra-fast optimizer.

    • In cryptography, the annealer can resolve basis mismatches and phase states deterministically, mimicking QKD-style reconciliation in silicon.
    • In machine learning, the same logic can perform hardware Gibbs sampling or energy-based inference orders of magnitude faster than CPU/GPU software.
    • In operations research, ROOM’s one-cycle collapse enables real-time constraint solving on embedded systems where power and latency matter.

    🚀 Looking Ahead

    The next generation of ROOM prototypes (on our Cyclone-V FPGA testbed) will expose an Avalon interface for annealer control — allowing host CPUs to define energy terms, seeds, and collapse policies through simple registers.

    We see the ROOM Annealer as the missing bridge between digital determinism and physical randomness — a way to get the benefits of stochastic optimization without leaving the world of synchronous logic.

    QSymbolic is developing ROOM (Read-Only-Once Memory) as a foundation for post-algebraic cryptography, secure key generation, and hardware optimization. To learn more or discuss collaborations, contact frank@qsymbolic.com.

  • ROOM: Read Only-Once Memory (Verilog Reference Implementation)

    ROOM (Read-Only-Once Memory) is a post-algebraic, quantum-inspired cryptographic primitive developed by QSymbolic LLC.

    Modeled after a quantum measurement enforcing the no-cloning theorem, ROOM ensures that a stored value (e.g., a cryptographic key) can be read once only. On the first valid access, the value is released and the register collapses irreversibly within the same clock cycle. All subsequent reads return obfuscation (pseudorandom or noise-influenced replacement values).

    This repository provides the reference Verilog modules for ROOM as described in the Post-Algebraic Cryptography patent filings.

    https://github.com/fcunnane/QSymbolic

    ⸻

    ✨ Key Features • Read-once enforcement — secrets collapse on first qualified read. • Metadata gating — access requires matching basis, phase, tags, or timing. • Collapse latch — same-cycle disable after release. • Obfuscation source — pseudorandom or entropy-derived replacement values. • Peer-linked collapse — entangled cells propagate collapse for group rekeying. • Entropy harvesting — collapse jitter and metastability seed RNGs.

    ⸻

    📂 Contents • collapse_cell.v — core ROOM cell with collapse latch + obfuscation. • metadata_collapse_register.v — adds metadata predicates (basis, phase, tags). • collapse_register_entangled.v — peer-linked collapse across cells. • collapse_register_keyexchange.v — ephemeral key release with KDF. • qkd_collapse_register.v — BB84-style collapse emulation. • qkd_entangled_pair.v — entangled pair register (E91-style). • mdi_qkd_top.v — measurement-device-independent (MDI) protocol demo. • collapse_rng.v — collapse-derived entropy source. • testbench.v — simulation environment.

    ⸻

    🔒 Security Properties • Post-algebraic & post-quantum: independent of lattice/coding hardness assumptions. • Quantum-inspired: enforces a no-cloning principle at the hardware/software level. • QKD-like intrusion detectability: unauthorized reads collapse secrets into noise, measurable via error rates. • Low power, high efficiency: collapse + KDF cycle costs far less than lattice-based PQC or optical QKD. • Composable: works in FPGA, ASIC, SIM/secure elements, and software.

    ⸻

    🛰️ Applications • Mobile / 6G radios — ultra-low-latency ephemeral rekeying. • Satellites & swarms — low-power, high-efficiency key release; peer collapse for group rekey. • Cloud KMS / HSMs — tamper-resistant ephemeral API/tenant keys. • IoT & secure boot — one-time provisioning and firmware authentication. • ZKPs & homomorphic encryption — collapse-backed entropy for protocols.

    ⸻

    📜 License

    This project is licensed under the PolyForm Noncommercial License 1.0.0. • ✅ Free for personal, research, academic, and other noncommercial purposes. • 🚫 Not permitted for commercial use (products, services, paid offerings) without a license.

    For commercial licensing (semiconductors, telecom, satellite, defense, etc.), please contact: QSymbolic LLC — Francis X. Cunnane III 📧 frank@qsymbolic.com | 🌐 qsymbolic.com

    ⸻

    https://github.com/fcunnane/QSymbolic

    ⚠️ Disclaimer: This software is provided “as is”, without warranty of any kind, express or implied.

  • 🛠 ROOM Enclaves Adoption Pathway

    Stage 1 – FPGA / Prototype (Now)

    • Form: Verilog modules on FPGA (DE10 Nano, dev boards).
    • Goal: Prove collapse-on-read, entanglement, and metadata enforcement in hardware.
    • Value: Establish novelty + patent protection + demo viability.
    • Users: Researchers, DARPA testbeds, cryptography labs.

    Stage 2 – PCIe / External Accelerator (1–3 years)

    • Form: ROOM implemented on a PCIe card (like GPUs or HSMs).
    • Goal: Provide a drop-in “ROOM Engine” for servers. Keys collapse in hardware, but host only sees wrapped or ephemeral outputs.
    • Value: Market entry for enterprises → “Hardware unclonable key accelerator.”
    • Users: Banks, defense contractors, cloud hyperscalers (pilot programs).

    Stage 3 – SoC Security Co-Processor (3–6 years)

    • Form: ROOM integrated as a security block inside SoCs, like AES or SHA accelerators.
    • Goal: Embed ROOM into ARM/Intel/AMD chipsets as a companion primitive for secure enclaves.
    • Value: On-die collapse means no bus exposure; faster adoption in consumer devices (phones, laptops).
    • Users: Mobile security (Apple Secure Enclave, Google Titan), TPM replacements.

    Stage 4 – CPU Enclave Integration (6–10 years)

    • Form: ROOM primitives available as ISA extensions (e.g., ROOM_LOAD, ROOM_ENTANGLE).
    • Goal: Standardize ROOM as part of trusted execution environments (Intel SGX vNext, AMD SEV, ARM TrustZone+).
    • Value: Cloud-scale adoption → ephemeral keys for SaaS, finance, military workloads.
    • Users: Cloud providers, defense, healthcare, governments.

    Stage 5 – Ubiquity (10+ years)

    • Form: ROOM cells as standard CMOS memory primitive, like SRAM or DRAM cells.
    • Goal: Every chip has ROOM at transistor level. Collapse enforced everywhere.
    • Value: Universal “no-cloning on read” security baseline → hardware guarantees instead of software policies.
    • Users: Everyone — from smartphones to satellites.

    🚀 Strategic Takeaway

    ROOM moves up the adoption ladder:

    FPGA → PCIe card → SoC security core → CPU enclave → universal primitive.

    At the CPU-enclave stage, ROOM becomes the de facto hardware standard for unclonable key lifecycles — the logical endpoint for post-algebraic cryptography.

  • CMOS Keys™: The Future of Post-Algebraic Cryptography

    For decades, digital security has relied on algebraic hardness assumptions: factorization, discrete logarithms, and lattices. From RSA to elliptic curves to today’s post-quantum algorithms, the foundation has always been mathematical. But what if the next leap forward doesn’t come from math at all?

    What if security came from the hardware itself?

    Enter CMOS Keys™ — a new class of hardware primitives that collapse on read, enforce forward secrecy by design, and represent the first wave of Post-Algebraic Cryptography.

    Why Algebraic Security Isn’t Enough

    • Cloneable: Traditional keys are just numbers in memory. If an attacker reads them once, they can copy them forever.
    • Persistent: Keys stored in RAM, flash, or HSMs remain available until explicitly erased.
    • Algebra-dependent: Even “post-quantum” cryptography still assumes attackers are limited by certain math problems.

    These weaknesses all share a common trait: they treat keys as static data.

    The CMOS Keys Breakthrough

    CMOS Keys are not just data — they are circuits.

    A CMOS Key is instantiated inside silicon as a read-once memory cell. The moment it is accessed under the correct condition, it delivers its true value and then collapses into an obfuscated state, enforced at the transistor level.

    • 🔒 Read-Once: Keys can only be accessed a single time.
    • ⏳ Ephemeral: The act of use destroys the original.
    • 🛡 Unclonable: There is no static copy to extract or leak.
    • ⚡ Standard CMOS: Built entirely in existing semiconductor processes — no exotic hardware required.

    Post-Algebraic Cryptography

    We call this paradigm Post-Algebraic Cryptography because it steps outside the algebraic assumptions that have defined cryptography since the 1970s.

    Instead of securing secrets with mathematical difficulty, CMOS Keys secure them with physics and circuit behavior. A post-algebraic system does not care whether quantum computers succeed or fail, because its guarantees are not based on factoring, lattices, or any other algebraic structure.

    This is cryptography anchored in hardware collapse, not algebra.

    Applications of CMOS Keys

    • Forward Secrecy by Default: Every key collapses after first use, enforcing perfect forward secrecy without complex protocols.
    • Tamper-Resistant Hardware: CMOS Keys can’t be cloned, even with invasive attacks.
    • HSMs and TPMs: A new generation of security modules where keys are never persistent.
    • Cloud Security: Ephemeral keys that disappear after use, eliminating long-term exposure.
    • Military & Critical Infrastructure: Secure communication without relying on fragile algebraic hardness assumptions.

    Why Now?

    The world is racing to prepare for the post-quantum era, but most efforts still depend on unproven algebraic hardness. CMOS Keys are different: they are manufacturable in today’s silicon fabs, deployable in existing security architectures, and provably eliminate entire categories of attack by design.

    This isn’t just post-quantum.

    This is post-algebraic.

    Closing Vision

    “We’ve tried to protect secrets with math for 50 years. CMOS Keys protect them with physics. This is bigger than post-quantum — this is Post-Algebraic Cryptography.”

    The age of storable keys is ending. The age of CMOS Keys is beginning.

  • 📣 Patent Filed: ROOM – Read Only Once Memory

    July 31, 2025

    Virginia Beach, VA — Today, QSymbolic LLC is proud to announce the official patent submission for ROOM – Read Only Once Memory, a new hardware memory primitive built for the post-quantum era.

    ROOM is a secure memory architecture that enforces one-time readout using deterministic CMOS logic. Upon first access, each ROOM register collapses irreversibly, making the stored value permanently inaccessible. No reset. No reuse. No cloning.

    🔐 Built for What’s Next

    In conventional systems, memory is reusable by design — and that’s a problem when the data is a cryptographic key, authentication secret, or ephemeral credential. ROOM addresses this by bringing quantum-inspired collapse behavior to classical hardware.

    ROOM is engineered for:

    • Air-gapped, tamper-evident key delivery
    • Post-quantum and post-algebraic cryptography
    • Secure embedded provisioning in zero-trust environments
    • Symbolic entropy handling and deterministic collapse control
    • Emulation of QKD-like guarantees in purely classical logic

    Using a suite of patented primitives — including collapse enforcement, metadata-gated access, and entangled register behavior — ROOM establishes a new security baseline for sensitive memory.

    📄 What’s Included in the Patent

    Filed as a Continuation-in-Part (CIP) of the Triangle Symbolic Processing Framework (TSPF), the ROOM patent application includes:

    • Collapse Primitive: Memory registers that self-destruct on read, enforceable in the same clock cycle
    • Metadata Primitive: Registers that conditionally permit readout based on time, identity, or phase
    • Entanglement Primitive: Interdependent memory collapse across registers for structured correlation
    • Hardware-based key exchange workflows for post-quantum delivery
    • Real-world implementation details in CMOS, ASIC, and FPGA

    The system is suitable for integration into hardware security modules (HSMs), trusted platform modules (TPMs), secure microcontrollers, and defense-grade embedded platforms.

    🧩 No Trademark, by Design

    QSymbolic is not trademarking the word ROOM — intentionally.

    We believe ROOM should become an industry-standard term, like RAM or ROM. We invite researchers, engineers, and hardware vendors to adopt the term ROOM to describe any read-once, collapse-enforced memory structure — with or without our reference design.

    ROOM is not just a technology. It’s a behavior.

    ⚙️ What Comes Next

    Now that the patent has been filed, QSymbolic will begin:

    • Demonstrating ROOM on FPGA
    • Publishing the ROOM specification and open HDL components
    • Preparing submissions to peer-reviewed venues (e.g., NDSS 2026)
    • Exploring licensing and partnership opportunities in secure hardware sectors

    ROOM will also anchor QSymbolic’s upcoming secure key exchange appliance and memory security IP core offerings.

    For inquiries, partnerships, or technical details, contact frank@qsymbolic.com or visit qsymbolic.com.

    QSymbolic – Memory You Can’t Clone™

    PATENT PENDING

  • 🔐 CollapseRAM: The First Verifiable Entropy Oracle for Post-Quantum Key Generation

    Trust is Broken. Key Generation Shouldn’t Be.

    In a world moving rapidly toward post-quantum cryptography, we’re still stuck with one of the oldest assumptions in security: just trust the box. Whether it’s an operating system, a TPM, a cloud HSM, or even a certified TRNG, we’ve been conditioned to believe that whatever spits out a cryptographic key has done so honestly.

    But where’s the proof?

    What we need — and what we’ve never truly had — is a verifiable entropy oracle: a system that not only generates cryptographic keys, but proves they were generated uniquely, freshly, and securely.

    That’s what CollapseRAM is.

    CollapseRAM: A New Class of Secure Entropy Hardware

    CollapseRAM is a novel architecture based on symbolic computation. It generates entropy using symbolic registers in ambiguous states — akin to quantum superposition — and collapses them in a one-way, read-once fashion. Once a register collapses, its value is locked, and further reads yield a fixed bit.

    But here’s the crucial difference: every collapse event is recorded, hashed (e.g. using SHA3), and signed or timestamped to produce a verifiable proof of entropy generation. This isn’t just keygen — this is provable entropy origination.

    Each key generated by CollapseRAM:

    • Is derived from non-reversible symbolic collapse
    • Cannot be reused or replayed
    • Is never exposed to system memory
    • Is optionally wrapped with a post-quantum public key (e.g. Kyber)
    • Produces a tamper-evident hash proof (e.g. of the form SHA3(key + timestamp + session_id))

    The result is a key you can use — and prove was never seen, reused, or faked.

    Why This Matters More Than Ever

    For years, we’ve relied on HSMs, smartcards, and kernel APIs like /dev/random or getrandom() to give us our cryptographic backbone. These systems may be certified, but they all rely on the same basic trust model: if it came from the black box, it must be good.

    CollapseRAM challenges that model.

    With verifiable entropy collapse, you no longer need blind trust. Instead, you get:

    • Proof that your key was fresh
    • Proof that your key was unique
    • Proof that your key never existed outside the appliance

    That level of assurance isn’t just valuable — it’s necessary in a world where quantum threats are real and state-level actors have both motive and means to manipulate entropy.

    Aligning With NIST and NSA Standards

    CollapseRAM is designed to work within existing cryptographic frameworks:

    • SP 800-90B: Meets and exceeds entropy source requirements
    • FIPS 140-3: Enforces strict key lifecycle and separation
    • FIPS 203 (Kyber): Wraps keys in post-quantum-safe envelopes
    • CNSA 2.0: Suitable for use in national security systems

    Whereas traditional HSMs may be compliant, they do not expose any verifiable entropy audit trail. CollapseRAM offers both compliance and accountability.

    This Is More Than an HSM

    CollapseRAM isn’t just another crypto box. It’s a fundamentally new concept:

    • A read-once entropy appliance
    • A symbolic register processor
    • A verifiable key oracle
    • A quantum-resistant trust anchor

    It can generate AES-256 keys, deliver them wrapped via Kyber, and record a log that proves each key was freshly born and securely bound to the recipient.

    And it works with today’s Internet stack — including TLS, VPNs, and encrypted messaging — while being future-ready for PQ-TLS and PQ-VPN.

    What’s Next

    CollapseRAM is real. It’s working. And it’s heading to NDSS.

    If you’re a cryptographer, developer, or standards contributor, I invite you to explore the idea of provable entropy. Whether you’re building encrypted messaging apps, zero-trust infrastructure, or post-quantum VPNs, CollapseRAM can change the way you think about key generation.

    Because in the end, it’s not just about trusting the box — it’s about building a world where we don’t have to.

  • Collapsing the Keyspace: Symbolic Grover’s Attack on AES

    In the search for post-quantum cryptanalytic techniques, we’ve been conditioned to think in terms of massive compute, quantum interference, and costly brute-force attacks. But what if a symbolic approach—something in between algebra and intuition—could collapse the keyspace without ever needing a qubit?

    That’s the premise behind Symbolic Grover’s: a hybrid method of key recovery that mimics the behavior of Grover’s quantum search algorithm using classical logic and symbolic collapse scoring. We recently applied this method to AES-128, and the results are worth exploring.

    The Problem: 3 Unknown Bytes in AES-128

    We assume partial knowledge of a 128-bit AES key, with 13 bytes known and 3 bytes unknown. That creates a remaining search space of:

    256^3 = 16,777,216 possible keys

    A traditional brute-force attack would search all ~16.7 million combinations. Our goal was to recover the full key using symbolic entropy collapse and targeted refinement, avoiding a full sweep.

    The Method: Symbolic Collapse with Entropy and Phase

    We approached the problem using what we call Symbolic Grover’s, which works like this:

    1. Sampling:
      Generate 250,000 key candidates by randomly filling in the unknown ∆ bytes. This is only 1.5% of the full keyspace.
    2. Collapse Scoring:
      Each candidate is tested against the known ciphertext. After decryption, we score it based on the entropy profile of the plaintext—how closely it resembles natural language or structure.
    3. Amplitude & Phase Logic:
      Inspired by Grover’s quantum search, each symbolic candidate is given an “amplitude” and a “phase.” Good candidates undergo constructive phase alignment, boosting their symbolic amplitude. Poor candidates destructively interfere.
    4. Collapse:
      The candidates are ranked by amplitude. The highest-amplitude guess is selected as the symbolic best match.

    The Results

    Test 1:

    • True Key: 3 bytes unknown
    • Best Symbolic Match: 13 of 16 bytes correct
    • Refined Brute-Force: Full key recovered after searching only a narrow range around the top symbolic guess

    Test 2:

    • New Key, New Plaintext
    • Same process repeated
    • Symbolic Grover’s again converged on a 13/16 match
    • Full key recovered through refinement

    In both cases, the symbolic engine collapsed a 16.7 million keyspace down to 250,000 ranked candidates, then honed in on the correct key with only minimal refinement.

    Why It Matters

    This isn’t brute-force with a fancy name.

    Symbolic Grover’s doesn’t just guess—it learns from entropy collapse and steers future guesses toward convergence. It applies symbolic weights (amplitudes), interference logic (phase), and targeted collapse to prioritize promising regions of the keyspace.

    With just 1.5% of the keyspace searched, we were able to:

    • Converge consistently to near-correct keys
    • Avoid wasteful enumeration
    • Collapse symbolic ∆ values into concrete byte guesses

    This symbolic reasoning model echoes the structure of quantum Grover’s—but it runs on deterministic, classical logic.

    Where This Leads

    Symbolic Grover’s is only one part of a larger project we call CollapseRAM—a memory system built on symbolic entropy, read-once collapse behavior, and non-clonable registers. In CollapseRAM, symbolic registers (∆, θ) simulate collapse, interference, and entanglement without quantum hardware.

    AES key recovery is just the beginning. Next up:

    • Symbolic attacks on Kyber (post-quantum lattice encryption)
    • Collapse-guided BIP38 wallet recovery
    • In-memory QKD-style key exchange

    Final Thought

    If symbolic collapse can guide us to AES keys without brute-force…

    what else can it collapse?

    PATENT PENDING

  • Why Symbolic Grover Beats Quantum Grover (When It Comes to Breaking Keys Today)

    Grover’s algorithm is a superstar in quantum computing — a theoretical engine that can search an unstructured database in O(√N) time. In the world of cryptography, it’s often cited as a future threat to symmetric encryption like AES. The catch? You need a fully working quantum computer with thousands of stable qubits and error correction. Not exactly something you can download and run on your laptop.

    But what if we could simulate Grover’s quantum behavior using only classical tools? What if we could score key candidates not by blindly searching, but by measuring how close each candidate “feels” to the right one?

    That’s what Symbolic Grover does.

    Born from a geometry-based collapse model, Symbolic Grover uses classical computation to simulate the structure and behavior of quantum search — without quantum gates. It runs today, on real hardware, and it’s already solving real cryptographic problems in ways quantum Grover can’t.

    What Is Grover’s Algorithm?

    Grover’s algorithm is a quantum search procedure that gives a quadratic speedup over brute-force search. In simple terms, if a classical search takes N steps, Grover can do it in about √N using amplitude amplification. For example, recovering a 64-bit key might take 2^64 steps on a classical computer, but only 2^32 queries with Grover’s algorithm on a quantum machine.

    But there’s a problem: Grover is real only in theory for now. Running it at scale requires reliable, fault-tolerant quantum computers — something we don’t yet have. Even if you have a few hundred noisy qubits, running real-world cryptanalysis with Grover is out of reach.

    Enter Symbolic Grover

    Symbolic Grover doesn’t rely on quantum states or unitary operators. Instead, it uses symbolic uncertainty (∆) and resonance markers (θ) to model ambiguity and convergence — in effect, simulating how a quantum algorithm would focus in on the right answer.

    Imagine a symbolic key pattern like this:

    TopSecretKEY∆∆∆∆

    That tells the engine: the first 12 bytes of the AES key are known, but the last 4 bytes are uncertain — they collapse from ∆ to a specific value based on how well they “resonate” with the correct ciphertext.

    By assigning a collapse score to each symbolic candidate, Symbolic Grover ranks keys not just by yes/no correctness, but by how close they are to the real thing. It’s a symbolic analog to quantum amplitude — but one that runs on GPUs today.

    Symbolic Grover vs. Grover’s Algorithm

    FeatureGrover’s AlgorithmSymbolic Grover
    HardwareQuantum computerCPU/GPU (classical hardware)
    ComplexityO(√N) theoreticalO(√N) empirical (with ranking)
    ResultExact match onlyRanked closeness + candidates
    Fault toleranceFragileRobust
    Runs todayNoYes
    Use in cryptanalysisTheoretical futurePractical now

    Symbolic Grover is not faster than quantum Grover in theory. But it has a critical advantage: it exists. It works now. And that makes it more powerful than any quantum algorithm that lives purely in white papers.

    Case Study: AES Key Recovery

    Let’s say you know the first 12 bytes of an AES-128 key, and want to recover the last 4 unknown bytes. That’s a 32-bit keyspace, or about 4.29 billion possibilities.

    Symbolic Grover can scan a million randomly sampled keys with ∆∆∆∆ in a few seconds. Each candidate is scored based on symbolic collapse behavior — not just bitwise distance, but convergence logic rooted in triangle ambiguity.

    In multiple experiments, Symbolic Grover returned keys that were within 1 or 2 bytes of the correct solution — significantly closer than random guessing. From there, a short guided brute-force sweep completed the recovery.

    All this happens with no qubits, no amplitude interference, and no quantum oracle. It’s pure collapse logic — and it works.

    Why Symbolic Grover Matters

    Symbolic Grover isn’t just a cute classical approximation of a quantum algorithm. It’s a new paradigm — part of a broader family of post-algebraic computation tools that use ambiguity, resonance, and symbolic logic to simulate quantum behavior without the hardware.

    It’s transparent. You can see how close your guess is, and why. You can rerank candidates, guide brute-force intelligently, and even learn from entropy patterns in the keyspace. None of this is possible with pure Grover, which either gives you the answer or doesn’t.

    Symbolic Grover is also deeply extensible. It fits into a broader ecosystem of tools like CollapseRAM, symbolic QKD, and entropy-based cryptographic analysis. It can be embedded into AI, used to attack hybrid schemes, or extended to password hashes and memory leaks.

    Conclusion

    Quantum Grover is theoretically optimal. But Symbolic Grover is useful today.

    In a world where we can’t yet build stable, large-scale quantum machines, symbolic methods offer a powerful alternative. They’re not just stopgaps. They’re stepping stones to a whole new category of computation — one where geometry, logic, and symbolic collapse replace the need for entanglement and interference.

    Symbolic Grover won’t make quantum computing obsolete. But it just might make real cryptanalysis a lot more interesting — right now.

    PATENT PENDING

  • Post-Algebraic Key Exchange: Way Beyond Post-Quantum

    For decades, cryptographic security has depended on algebra. Whether it’s factoring large primes, computing discrete logarithms, or solving structured lattice problems, most encryption systems rely on the hardness of mathematical problems. This includes not only traditional protocols like RSA and Diffie–Hellman, but also newer post-quantum candidates like Kyber, which is based on structured lattices and modular polynomials.

    But all of these systems share a common vulnerability: they can be attacked by algorithms that exploit their underlying mathematical structures. Shor’s algorithm, for instance, breaks RSA and elliptic curve cryptography on a sufficiently powerful quantum computer. And even the most promising lattice-based systems are still fundamentally mathematical, meaning they remain within reach of future algebraic attacks, whether quantum or classical.

    This raises a natural question: what if we moved beyond algebra entirely? What if our cryptographic systems didn’t rely on math at all—not even hard math, just no math?

    This is the foundation of a new concept: post-algebraic key exchange. Rather than depending on structured mathematical problems to secure a key exchange, post-algebraic systems like CollapseRAM derive shared secrets from symbolic logic, entropy, and ephemeral memory behavior. These systems don’t use exponentiation, modular arithmetic, or polynomial operations. Instead, they use entropy inputs—such as time-stamped randomness—combined with symbolic memory registers that collapse in a one-time, irreversible way.

    Here’s how it works in practice. Each party generates a blob of entropy, such as a SHA256 hash of current time, a device-specific fingerprint, and some randomness. They then derive a symbolic register based on a shared seed or session context. This register doesn’t store values in the usual sense—it represents a symbolic structure that collapses when read. Both parties combine their entropy blobs, hash them, and use that combined value to collapse the register. The result is a shared key. But—and this is key—once the collapse occurs, the register is consumed. It can’t be read again. It can’t be reused. This is known as “write-once, read-once” or WORO memory logic.

    Because there’s no algebra involved, there’s nothing to invert. No equation to solve. No structure to attack. Even if an attacker records the entropy blobs, they cannot retroactively reconstruct the symbolic register or the exact collapse behavior unless they had access to it before it collapsed.

    This makes post-algebraic systems fundamentally different from—and arguably more resilient than—existing cryptographic schemes. They are not just post-quantum. They are post-algebraic. They do not attempt to resist mathematical inversion. They avoid it altogether.

    CollapseRAM is currently the leading example of this paradigm. It enforces ephemeral key exchange by design. It produces keys that are bound to entropy and symbolic state, not to algebraic coordinates. It’s fast, lightweight, memory-driven, and doesn’t require any special hardware to operate. In use cases where key reuse is dangerous or long-term storage of private keys is risky—such as in zero-trust environments, stateless servers, or secure enclave communication—post-algebraic systems offer real and practical benefits.

    There’s still much work to be done. We need formal security models for these kinds of systems, and new analysis tools that don’t rely on algebraic reasoning. But the promise is clear. If cryptography is going to survive the coming decades, it may not just need to evolve beyond current mathematics. It may need to leave math behind altogether.

    CollapseRAM is one step in that direction.

    PATENT PENDING