Quantum Instruction Scheduling With Control Pulse Optimization
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Solution Overview
Problem
Existing quantum computing systems face significant challenges in reducing computation latency due to inefficiencies in classical compilation techniques, which lead to a mismatch between logical and physical operations, especially as the number of qubits increases, resulting in exponential decay of output fidelity and potential decoherence.
Innovation Solution
A quantum compilation engine that optimizes quantum programs by breaking down logical instructions into smaller blocks, utilizing commutativity to maximize parallelism, and generating optimized control pulses, effectively mapping instructions onto qubits to reduce latency and align with physical hardware capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum programs are compiled into quantum assembly instructions using conventional gate-based approaches, then a universal set of operations is achieved, but computation latency increases and output fidelity decays
Solution Approach 1:
The patent segments the quantum program compilation process into multiple optimization stages: initial quantum assembly compilation, dependency graph generation, instruction aggregation into blocks, and parallel execution scheduling. This segmentation allows each stage to optimize specific aspects independently, reducing overall latency while maintaining universality through the modular architecture.
Solution Approach 2:
The patent performs preliminary actions by pre-computing dependency graphs and commutativity relationships between instructions before execution. By analyzing and caching these dependencies in advance, the system eliminates redundant runtime checks and enables faster parallel execution without sacrificing the ability to handle diverse quantum operations.
2Ease of operation
If quantum assembly instructions are translated into control pulses for physical hardware, then operations can be executed on quantum processors, but latency increases due to the translation overhead and mismatch between logical and physical operations
Solution Approach 1:
The patent introduces an intermediary optimization layer between quantum assembly and control pulse generation. This layer aggregates instructions into blocks, optimizes their execution order based on commutativity, and generates consolidated control pulses. This intermediary stage reduces the total number of pulse generation operations and eliminates redundant translations, significantly reducing latency while preserving execution accuracy.
3Quantity of substance
If the number of qubits is increased to solve more complex problems, then computational capability improves, but output fidelity decays exponentially due to decoherence
Solution Approach 1:
The patent implements continuous optimization throughout the compilation and execution pipeline. By continuously analyzing instruction dependencies, dynamically aggregating instructions into executable blocks, and optimizing control pulse generation in real-time, the system maximizes the utilization of available qubit coherence time. This continuous optimization ensures that computational tasks are completed as quickly as possible, maintaining high fidelity even as qubit counts increase.
4Productivity
If quantum programs are optimized for parallel execution, then computation latency is reduced, but device complexity increases due to scheduling and aggregation overhead
Solution Approach 1:
The patent changes key parameters of the compilation system to balance parallelism and complexity. It introduces block size parameters, aggregation thresholds, and scheduling heuristics that can be tuned based on available quantum hardware resources. By parameterizing the optimization process, the system achieves high parallelism when resources permit while automatically reducing complexity for smaller or more constrained systems, maintaining adaptability across different quantum processors.
Data Source
AI summary
A quantum computing system includes a quantum processor having a plurality of qubits, a classical memory, and a classical processor. The classical processor is configured to compile a quantum program into logical assembly instructions in an intermediate language, aggregate the logical assembly instructions together into a plurality of logical blocks of instructions, generate a logical schedule for the quantum program based on commutativity between the plurality of logical blocks, generate a tentative physical schedule based on the logical schedule, the tentative physical schedule includes a mapping of the logical assembly instructions in the logical schedule onto the plurality of qubits of the quantum processor, aggregate instructions together within the tentative physical schedule that do not reduce parallelism, thereby generating an updated physical schedule; generate optimized control pulses for the aggregated instructions, and execute the quantum program on the quantum processor with the optimized control pulses and the updated physical schedule.