Quantum Source Coding with Sorting Networks
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Solution Overview
Problem
Existing quantum source coding protocols are impractical for near-term devices due to complex operations and significant overheads when compiled for general-purpose quantum computing architectures, and they require many qubits for lossless compression.
Innovation Solution
The development of quantum source coding methods using quantum sorting networks that are resilient to noise and can be implemented in a distributed fashion with logarithmic running time, reducing the need for extensive error correction and qubit usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing quantum source coding schemes are used, then quantum data compression can be achieved, but the operations become complex and the circuits become deep, requiring large fault-tolerant quantum computers
Solution Approach 1:
The patent divides the quantum sorting network into multiple stages (e.g., bitonic sort network with multiple comparator networks), where each stage processes a subset of qubits independently. This segmentation reduces the depth and complexity of individual circuit layers while maintaining the overall compression functionality through systematic progression through stages.
Solution Approach 2:
The patent employs dynamic qubit allocation where qubits are reassigned to different roles (message qubits vs. auxiliary qubits) at different stages of the sorting network. This dynamic reconfiguration allows the same physical qubits to serve multiple purposes, reducing the total number of qubits needed and simplifying the circuit structure.
2Reliability
If existing quantum source coding schemes are used, then compression can be achieved, but compiling to general-purpose quantum computing architectures leads to significant additional overheads and slowdowns
Solution Approach 1:
The patent replaces complex arithmetic operations with simpler quantum sorting network operations that are native to the hardware architecture. By using quantum comparators and swap gates instead of general-purpose arithmetic circuits, the implementation achieves better performance when compiled to actual quantum hardware architectures.
Solution Approach 2:
The patent optimizes circuit parameters such as the number of qubits, circuit depth, and gate operations by adjusting the sorting network configuration. This allows the algorithm to adapt to specific hardware constraints and achieve more efficient compilation to general-purpose quantum architectures.
3Reliability
If existing quantum source coding schemes are used, then compression can be achieved, but they require many qubits for lossless compression
Solution Approach 1:
The patent dynamically manages qubit allocation by transitioning qubits between active and inactive states during different stages of the sorting network. This allows the system to use fewer qubits overall by reusing the same physical qubits for different computational roles at different times, while still achieving lossless compression.
Solution Approach 2:
The patent implements a nested structure where auxiliary qubits are introduced only when needed at specific stages of the sorting network, and then discarded or reused. This nested allocation pattern reduces the peak qubit requirement while maintaining the functionality of lossless compression through the hierarchical structure of the sorting network.
Data Source
AI summary
A quantum source coding method includes: initializing a quantum register having a plurality of nodes: loading a unary-coded message into the quantum register; loading an address state into an address register of each message node of the quantum register; sorting the message nodes based on a data register such that the message nodes form a message-sorted sequence; and applying, for each message node at an end of the message-sorted sequence, a CNOT gate to the address state of each of the register's message nodes and output nodes. The method also includes: unsorting the message nodes; sorting the nodes based on the address register such that the nodes form a fully-sorted sequence; applying, for each pair of adjacent nodes in the fully-sorted sequence having the same address state, a CNOT gate to the data registers of each pair; and unsorting the nodes to return the fully-sorted sequence to the initial sequence.