Choice Coin Quantum

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Quantum

Choice Coin is starting a new open source software project for developing quantum software to integrate with blockchain technologies on Algorand. The project is open source and invites collaborators. This Post will serve as a resource for the project and a place where updates may be provided. Please also feel free to provide any thoughts, feedback, or critique below.

Choice Coin Quantum GitHub

Motivation

Quantum computing provides a solution for solving SHA-256 by means other than brute force, which will provide a cost-efficiency improvement in mining. Long term, a goal is to use the software developed to mine digital assets from Proof-of-Work blockchains and bridge those assets to Algorand.

Problem

Main Problem: How to solve SHA-256 with a quantum computer.

Sub-Problems:

  1. How is the the cryptographic problem broadcast to the Bitcoin network to validate new blocks?
  2. How are guesses submitted to the Bitcoin network to validate new blocks?
  3. How can the quantum computer be used to solve new blocks faster than classical computing systems?

Solution Approach

Our goal is to advance the edge in quantum cryptography for the purpose of mining digital assets with a quantum computer. The focus for the research will be on blockchain security innovation.

Quantum Computing

Quantum mechanics is the scientific discipline concerned with the motion and interaction of subatomic particles. Conceptually, Richard Feynman was the first person to discuss the intuition behind quantum computers, specifically to evolve computers from binary logic to a higher-order logic using quantum mechanical properties. The idea was based on the quantum mechanical principle, superposition. Superposition describes an instance where a subatomic particle occupies two independent spatial positions simultaneously. Feynman’s greatest idea was to exploit this principle to improve computational systems.

A quantum computer is a physical system harnessing quantum effects to perform computation. Quantum computers differ from classical computers because of the way in which they process information. Classical computers process information with bits, a binary representation. However, quantum computers process information with qubits, which represent information in a complex vector space.

The qubit is an innovation advancing the goal to improve the efficiency and power of classical computing methodologies with quantum mechanics. A qubit may represent a zero, one, or zero and one simultaneously in a state of superposition. The qubit allows for faster computing and less electrical
power consumption compared to its classical counterpart.

Adiabatic Quantum Computers

Adiabatic quantum computers (AQCs) are supercomputers harnessing natural quantum state evolution to perform computation. Instead of using Silicon like traditional computer chips, the quantum chip uses a metal called Niobium. The Niobium is looped throughout the chip, connecting the qubits and acting as a superconducting metal where each loop models a quantum spin. The chip is cooled to the near zero Kelvin temperature and becomes a superconductor, a metal with properties including zero electrical resistance and magnetic flux fields. The superconducting properties allow the chip to manipulate quantum mechanical physics and eliminate noise during the computational process.

Gate Model Quantum Computers

The second type of quantum computer is the Gate Model Quantum Computer (GMQC). In contrast to AQCs, which utilize a quantum state’s natural evolution, GMQCs directly control quantum state evolution. In this approach, quantum circuits are engineered from electrical and mechanical components to create a computational circuitry using qubits. Further, the qubits are acted upon by sequences of logical gates that are the compiled representation of an algorithm. The GMQC includes two key elements, the quantum circuit and gate transformation.

Photonic Circuit Board

Photonic Quantum Computers (PQCs) are the newest type of quantum computer. PQC hardware is developing on research demonstrating a qubit can be represented by polarized photonic spin. A photon is a single light particle, which has no charge and zero rest mass. The relationship between electron spin and photonic polarization may be explained analogously. For example, MIT researcher Mihika Prabhu, experimentally demonstrated success for quantum sampling on a PQC.

Resources

Quantum Patents

Quantum Machine Learning: A Patent Review

Blockchain Post-Quantum Security and Legal Economics

Search Sample Equivalence

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Some initial ideas may be found in Quantum Integrations.

Resources

Accelerating recurrent Ising machines in photonic integrated circuits

Towards optimal capacity-achieving transceivers with photonic integrated circuits

An Introduction to Quantum Machine Learning

The quest for a Quantum Neural Network

Prediction by Linear Regression on A Quantum Computer

Predictive Quantum Programming

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Update

Choice Coin Quantum Demo #1. Running classical hashing algorithms on through D-Wave’s Quantum Leap interface.

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Did the Quantum Computer just break Bitcoin?!

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@bhaney44 What makes you to think so?
See e.g.: Quantum computers a long way from hacking SHA-256 algorithm

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Webber’s math is wrong as it pertains to the minimum needed and so is the information about IBM’s quantum computer having the most qubits with 127. I’ve run several calculations that suggest it would be possible to crack SHA-256 with ~896 qubits. D-Wave’s adiabatic quantum computer has 2,000 qubits. Other models, such as a Psi Quantum have over 1 million qubits. I think it’s possible to invert SHA-256 right now. The issue I am currently working through is importing the quantum software into the classical hashing algorithm.

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Update

Choice Prize. 1 million Choice to the first complete refute to my proof for BSD.

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Update

Video demonstrating Bitcoin post-quantum security against consensus attack from quantum neural networks. Interestingly, Bitcoin mining is not just about finding the right nonce, it is about finding a nonce and a version number, that when aggregated with the block header data produces an appropriate hash.

Block Header

        Nonce = str(i)
        Version = "00100000"
        hashPrevBlock = "0000000000000000000571509eac4819dae8ff2c320c487cfd612d36db7ffe1a"
        hashMerkleRoot = "e320b6c2fffc8d750423db8b1eb942ae710e951ed797f7affc8892b0f1fc122b"
        Time = "1672956895"
        Bits = "1708417e"

Here, the Nonce variable is presumed random, but as shown in Bitcoin block headers, the Version variable is also random. So, there are two randomized variables in Bitcoin mining, not just one.

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We’ve seen growth in the past year on Choice Coin Quantum. Essentially, we have been able to build a neural network that predicts the next nonce in the block hash for Bitcoin and successfully run the program on the quantum computer. However, the predictions have not yet been accurate, producing a winning nonce.

That said, we ran into an unexpected problem with the Bitcoin hash algorithm. Not only is the nonce value random, but the version number is also random. So, this essentially means that Bitcoin may be post-quantum secure in terms of mining. We have another direction to see if it’s possible to accurately predict the next nonce that we are currently pursuing. Ultimately, the goal is to mine Bitcoin algorithmically and bridge the Bitcoin to Algorand for the benefit of the Choice Coin project.

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Incredible. What number of qubits is required?

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Thanks. I’m running it through the D-Wave Leap Cloud now. I think they are currently at 5,000 quibits.

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