Automated Quantum Program Synthesis with Deep Reinforcement Learning
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Solution Overview
Problem
Existing technologies face challenges in efficiently generating quantum programs for quantum computers, particularly in solving optimization problems, due to the lack of automated and optimized processes for synthesizing quantum logic circuits using classical AI systems.
Innovation Solution
The use of classical artificial intelligence systems, specifically neural networks trained through deep reinforcement learning, to iteratively add quantum logic gates to quantum logic circuits, selecting gates based on their relative likelihood of improving the program, and optimizing them for specific quantum resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated synthesis of quantum programs is implemented, then productivity and efficiency in generating quantum logic circuits is improved, but device complexity and difficulty of operation increase due to integration of classical AI systems with quantum computing resources
Solution Approach 1:
The patent employs a classical AI system as an intermediary between the user's high-level problem description and the quantum computer's low-level execution requirements. The AI synthesizer acts as a mediator that automatically translates optimization problems into quantum logic circuits, eliminating the need for users to manually design complex quantum algorithms while managing the complexity transition between classical and quantum domains.
2Ease of operation
If manual design of quantum logic circuits is used, then ease of operation is maintained for simple cases, but productivity and manufacturing precision deteriorate due to time-consuming manual synthesis processes
Solution Approach 1:
The system enables self-service by allowing the classical AI synthesizer to automatically generate, optimize, and compile quantum logic circuits without requiring manual intervention from quantum programming experts. The AI system serves itself by iteratively improving circuit designs through reinforcement learning, automatically selecting optimal quantum gates and configurations based on the problem requirements, thereby maintaining operational simplicity while dramatically increasing productivity.
3Manufacturing precision
If optimized quantum programs are generated through AI synthesis, then manufacturing precision and reliability of quantum logic circuits are improved, but loss of time increases during the automated synthesis and training process
Solution Approach 1:
The patent applies preliminary action by pre-training the AI synthesizer on a dataset of quantum logic circuits and optimization problems before actual use. This offline training phase allows the system to learn optimal synthesis strategies in advance, so that during runtime, the AI can rapidly generate accurate quantum programs without requiring extensive real-time computation. The preliminary training invests time upfront to reduce synthesis time and improve precision during operational use.
Data Source
AI summary
In a general aspect, a quantum program can be automatically synthesized or compiled. In some implementations, a discretized state space is obtained. The discretized state space includes a plurality of states for one or more qubits of a quantum processor. A discrete action space is obtained. The discrete action space includes a plurality of unitary operations for the one or more qubits of the quantum processor. A policy that uses the discretized state space and the discrete action space to generate quantum programs for quantum state preparation is defined. A dynamic programming process is used to improve the policy. An initial state and a target state of the one or more qubits is identified. The policy is used to generate a quantum program to produce the target state from the initial state. The quantum program include a subset of the unitary operations in the discrete action space.