Adaptive Quantum Instruction Scheduling Across Q-PPUs to Limit Decoherence
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Solution Overview
Problem
Quantum computing applications like ray tracing and supersampling face challenges due to the need for multiple Grover iterations, which increase noise and decrease the probability of achieving correct results as the size of the quantum circuit increases, exacerbated by the fragile nature of qubits prone to decoherence.
Innovation Solution
An adaptive quantum instruction scheduler dynamically distributes quantum instructions to multiple quantum parallel processing units (Q-PPUs) based on measured probability and decoherence time, utilizing quantum teleportation to maintain stability and reduce noise, thereby improving the reliability of quantum circuits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple Grover iterations are performed to increase the probability of correct results, then the measurement accuracy improves, but the decoherence time decreases and noise increases
Solution Approach 1:
The patent divides the quantum circuit into multiple segments, each executed on a separate Q-PPU. By segmenting the overall computation into smaller sub-circuits that can be executed independently with fewer iterations, the patent reduces the noise and decoherence impact in each segment while maintaining the overall probability of correct results through multiple measurements and voting mechanisms.
Solution Approach 2:
The patent introduces classical intermediaries (measurement results and voting logic) between quantum computation segments. By measuring intermediate results after each Q-PPU execution and using classical voting to determine the final outcome, the patent avoids requiring a single long quantum circuit with many iterations, thereby reducing decoherence while maintaining measurement precision.
2Productivity
If the size of the quantum circuit increases to handle more complex computations, then the computational capability improves, but the noise increases and reliability decreases
Solution Approach 1:
The patent segments large quantum circuits into smaller sub-circuits that can be executed independently on multiple Q-PPUs. This segmentation reduces the noise accumulation that occurs in large monolithic circuits while preserving the computational capability to handle complex problems through parallel execution and result aggregation.
Solution Approach 2:
The patent transitions from a single-dimensional approach (one large quantum circuit) to a multi-dimensional approach by distributing computation across multiple Q-PPUs in parallel. This dimensional change allows complex computations to be performed with smaller individual circuits, reducing noise while maintaining overall computational capability.
3Measurement precision
If more quantum instructions are executed to improve computation accuracy, then the measurement precision improves, but the decoherence time is exceeded and errors increase
Solution Approach 1:
The patent segments the quantum instructions into smaller batches that can be executed within the decoherence time window of each Q-PPU. By dividing the total number of instructions into multiple smaller execution groups, the patent ensures each segment completes before decoherence occurs, while the aggregate of all segments achieves the desired computation accuracy.
Solution Approach 2:
The patent performs preliminary measurements and evaluations to determine the optimal number of iterations and instructions that can be executed within the decoherence time constraint. By pre-calculating safe execution limits and adjusting the computation plan accordingly, the patent maximizes computation accuracy within the available decoherence time window.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enhances the reliability and accuracy of quantum computing tasks by reducing errors and extending the decoherence time of qubits, leading to a higher probability of achieving correct results.
Implementation Method 1
While a classical bit can be in a state representative of 0 or a state representative of 1, a qubit can represent both states 0 and 1 at the same time. This is a quantum mechanical phenomenon known as superposition.
Implementation Method 2
Quantum computing relies on quantum mechanical phenomena such as superposition and entanglement to perform computations.
Data Source
AI summary
A quantum computing device includes a plurality of quantum parallel processing units (Q-PPUs) configured to execute a set of quantum instructions of a quantum application program. The quantum computing device includes an adaptive quantum instruction scheduler to dynamically distribute the set of quantum instructions to the plurality of Q-PPUs based, at least in part, upon a measured probability of a desired result of executing the set of quantum instructions of the quantum application program and a decoherence time of a qubit.