Title of the Invention System and Method for Harnessing Controlled, Non-Markovian Decoherence in Quantum Computing and Communication Using Engineered Noise Channels Cross-Reference to Related Applications This application claims the benefit of: U.S. Provisional Patent Application No. 63/751,887, filed January 31, 2025, titled “Bio-Inspired Platform for Enhanced Quantum Coherence”. U.S. Nonprovisional Patent Application No. 19/043,486, filed February 2, 2025, titled “Bio-Inspired Platform for Enhanced Quantum Coherence”. The disclosures of these applications are incorporated herein by reference in their entirety. Field of the Invention [0001] The present invention relates generally to quantum computing, quantum communication, and quantum sensing. Specifically, it discloses systems and methods for intentionally inducing and modulating controlled decoherence as a computational resource through engineered noise channels and dynamic qubit state transitions. Background of the Invention [0002] Traditional quantum systems treat decoherence as an error to suppress, requiring resource-intensive isolation (e.g., cryogenic cooling) or complex error correction. Recent advances in quantum biology and quantum annealing suggest that decoherence can be harnessed as a computational resource under controlled conditions. However, existing methods for inducing and controlling decoherence are limited in their precision, flexibility, and scalability. There is a need for more sophisticated techniques to engineer decoherence pathways and utilize them for practical quantum information processing tasks. Prior art systems typically attempt to minimize decoherence or use it in a limited, uncontrolled manner, failing to fully exploit its potential as a computational tool. Summary of the Invention [0003] The invention overcomes the limitations of the prior art by providing a system and method for inducing, controlling, and harnessing non-Markovian decoherence as a computational resource in quantum computing and communication. The present invention introduces: Decoherence Control Module: Engineered noise sources generate non-Markovian noise with precisely tailored frequency spectra and temporal profiles to induce task-specific decoherence in a plurality of qubits. Dynamic Qubit State Control: Synchronized transitions of qubits between states of varying coherence and decoherence are achieved using precisely controlled electromagnetic fields, enabling fine-grained control over decoherence pathways. Hybrid Quantum-Classical Interface: A specialized interface facilitates bidirectional communication between the quantum processor and a classical processor, utilizing partially decohered qubit states to generate analog signals for classical processing and receiving control signals to modulate the decoherence control module. Applications: This controlled decoherence is utilized for various applications, including quantum annealing for optimization, enhanced quantum sensing with nanotesla-scale precision, error mitigation through decoherence-encoded operations, and temporal data storage and retrieval. Detailed Description of the Invention System Architecture [0004] The system comprises: Quantum Processor: Utilizing superconducting qubits, trapped ions, photonic qubits, or solid-state defects such as NV centers in diamond, with each qubit modality requiring specific control and noise engineering techniques as detailed herein. Decoherence Control Module: This module is central to the invention and generates non-Markovian noise via a variety of techniques tailored to the specific qubit modality. Each of these noise generation methods allows for dynamic control over the noise characteristics, enabling the system to selectively induce decoherence in specific qubits or groups of qubits and to tune the decoherence rates and pathways. The choice of noise generation technique depends on the specific qubit modality and the desired decoherence characteristics. Examples include: Terahertz-Frequency Pulse Generation and Control: For superconducting qubits, utilizing Josephson junction arrays or other suitable sources to generate terahertz pulses with adjustable pulse duration (e.g., 1-100 picoseconds), frequency (e.g., 0.1-10 THz), and amplitude, synchronized to qubit resonance frequencies with sub-picosecond precision using a master clock signal distributed across the system. Phononic Lattice Design and Control: For solid-state defect or trapped-ion qubits, employing piezoelectric substrates (e.g., AlN, ZnO) with lithographically defined interdigitated transducers (IDTs) to generate surface acoustic waves (SAWs) in the frequency range of 0.1-5 GHz, creating phononic lattices with tailored lattice constants (e.g., 100 nm - 10 µm) and adjustable propagation directions. The amplitude and frequency of the SAWs are controlled by applying RF signals to the IDTs, with control signals generated by an arbitrary waveform generator (AWG) synchronized to the system’s master clock. Optical Noise Generation: For photonic or trapped-ion qubits, using amplitude and phase modulators to shape laser pulses with controlled frequency chirps and intensity profiles, inducing decoherence through photon-qubit interactions. Control parameters include chirp rate (e.g., 1-100 GHz/ns), pulse duration (e.g., 10 ps - 1 ns), and intensity modulation depth. Magnetic Noise Generation: Employing micro-coils integrated into the qubit chip to generate fluctuating magnetic fields with controlled bandwidth (e.g., 1 kHz - 1 MHz) and amplitude (e.g., 1-100 µT) to induce decoherence through magnetic interactions with the qubits. Dynamic Qubit State Control: Mechanism: Electromagnetic fields, generated by microwave or optical sources depending on the qubit modality, are used to entangle and disentangle qubits, and to drive transitions between coherent and decoherent states. These fields are precisely controlled in amplitude, phase, and frequency to achieve the desired qubit state manipulation. Control Parameters: For microwave control of superconducting qubits, typical pulse durations range from 10-100 ns, with frequency control in the 4-8 GHz range and amplitude control providing Rabi frequencies of 10-100 MHz. For optical control of trapped ions or photonic qubits, laser pulse parameters are adjusted similarly to the optical noise generation, with precise control over frequency, phase, and polarization to drive qubit transitions. Synchronization: A central control unit synchronizes the electromagnetic fields with the engineered noise generated by the decoherence control module, enabling precise temporal control over the decoherence process. Hybrid Quantum-Classical Interface: Signal Conversion: This interface utilizes specialized circuitry to convert the quantum information encoded in partially decohered qubit states into analog signals processable by classical CMOS circuits. This may involve measuring the population of specific qubit energy levels or detecting the phase difference between different qubit states. Signal Characteristics: The generated analog signals have specific voltage ranges (e.g., 0-1 V), bandwidths (e.g., 1 kHz - 1 MHz), and signal-to-noise ratios (e.g., >20 dB) tailored for optimal processing by the classical processor. Bidirectional Communication: The interface also receives control signals from the classical processor, which are used to adjust the parameters of the decoherence control module and the dynamic qubit state control, enabling a closed-loop feedback system for optimizing the decoherence process. Classical Processor Integration: The interface can be designed to be compatible with various types of classical processors, including CMOS processors, neuromorphic chips, and FPGAs, allowing for flexible integration with different classical computing architectures. Key Innovations [0005] Key innovations include: Engineered Noise Channels: Non-Markovian Noise: The system is specifically designed to generate and utilize non-Markovian noise, characterized by a frequency spectrum that is not flat and a temporal correlation function that decays non-exponentially. This type of noise allows for more complex and nuanced control over decoherence pathways compared to Markovian (memoryless) noise. The non-Markovianity arises from the structured environment created by the engineered noise sources, such as the specific modes of the phononic lattice or the tailored frequency spectrum of the terahertz pulses. Terahertz-Frequency Pulses (For Superconducting Qubits): As detailed above, these pulses are precisely synchronized to qubit resonance frequencies, with control over pulse shape, duration, frequency, and amplitude, enabling selective excitation of specific decoherence channels. Phononic Lattices (For Solid-State or Trapped-Ion Qubits): Piezoelectric substrates with lithographically defined patterns are used to create phononic lattices with specific band structures. By controlling the lattice parameters and the excitation of surface acoustic waves, the system can tailor the phononic environment to induce specific decoherence pathways in the qubits. Dynamic Qubit State Control: Transition Mechanism: As described above, precisely controlled electromagnetic fields are used to induce transitions between states of varying coherence. This enables the system to dynamically switch qubits between being more or less susceptible to the engineered noise, allowing for fine-grained control over the decoherence process. Feedback Loops: The hybrid interface enables the implementation of feedback loops, where the state of the qubits is monitored and the control parameters (e.g., noise characteristics, electromagnetic field parameters) are adjusted in real-time to optimize the decoherence process for specific computational tasks. Selective Addressing: The control system can selectively address individual qubits or groups of qubits, allowing for parallel operations and complex decoherence control sequences. Error Mitigation: Decoherence-induced parity oscillations are not just noise but can encode information about errors. The system is designed to interpret these oscillations, using them to implement corrective operations that mitigate the effects of unwanted decoherence, effectively using controlled decoherence to combat uncontrolled decoherence. Quantum Sensing: Zeeman-Effect Modulation: The system can utilize the Zeeman effect to detect external magnetic fields with high sensitivity. By monitoring changes in the decoherence rates of the qubits induced by the Zeeman shift, the system can detect nanotesla-scale magnetic fields. Enhanced Sensitivity: The controlled decoherence environment can be optimized to enhance the sensitivity of the qubits to external fields, leading to improved sensor performance compared to traditional methods. Applications [0006] The present invention may be used in the following applications: Quantum Annealing: The controlled decoherence can be used to drive the quantum system towards the ground state of a problem Hamiltonian, enabling efficient solutions to optimization problems. The non-Markovian nature of the noise allows for exploring the energy landscape in a more nuanced way than traditional annealing methods. NP-hard Problems: The system is particularly well-suited for tackling NP-hard problems, where the controlled decoherence can help escape local minima and find globally optimal or near-optimal solutions. Noise-Driven Annealing: The ability to precisely engineer the noise allows for implementing novel annealing schedules that leverage the unique properties of non-Markovian decoherence to enhance the annealing process. Temporal Data Storage: Information can be encoded in the decoherence timelines of the qubits, allowing for the storage and retrieval of information about past computational states. Predictive State Collapses: By analyzing the decoherence dynamics, the system can predict future state collapses, enabling proactive error correction or other actions based on anticipated future states. Time-Entangled Qubits: The system can utilize time-entangled qubits to store information across different time points, enabling more complex temporal data encoding schemes. Enhanced Quantum Sensing: As described above, the controlled decoherence environment can be used to enhance the sensitivity of quantum sensors, enabling the detection of ultra-weak magnetic fields or other physical quantities. Experimental Validation [0007] The mechanisms and applications described herein are based on established theoretical frameworks and experimental results in quantum physics, including the physics of open quantum systems, decoherence theory, and quantum control. Specific examples include: Theoretical studies of non-Markovian dynamics in open quantum systems. Experimental demonstrations of decoherence control in various qubit modalities. Numerical simulations of quantum annealing with controlled decoherence. Experimental results on the use of phononic lattices to manipulate quantum states. [0008] Further experimental validation will involve: Fabricating and characterizing prototype devices with integrated noise sources and qubit control systems. Performing experiments to demonstrate controlled decoherence in different qubit modalities. Implementing and testing quantum algorithms that utilize controlled decoherence for optimization, sensing, and other applications. Characterizing the performance of the system in terms of coherence times, gate fidelities, and computational accuracy. Alternative Embodiments [0009] Noise Source Variations: Besides terahertz pulses and phononic lattices, other engineered noise channels can be employed, including but not limited to: Optical Noise: Utilizing shaped laser pulses with controlled frequency and intensity modulation to induce decoherence through photon-qubit interactions. This can be particularly useful for photonic qubits or trapped ions. Magnetic Noise: Generating fluctuating magnetic fields via integrated micro-coils to induce decoherence through Zeeman interactions. The frequency spectrum and amplitude of the magnetic noise can be precisely controlled by adjusting the current in the coils. Engineered Environments: Creating environments with tailored thermal, electromagnetic, or chemical properties to influence qubit decoherence in a controlled manner. [0010] Qubit Modality Variations: The principles of the invention can be adapted to various qubit modalities beyond superconducting qubits and trapped ions, such as: Photonic Qubits: Decoherence can be controlled by manipulating photon paths and interactions, for example, using optical elements like beam splitters and phase shifters. Solid-State Defect Qubits: Utilizing defects in solid-state materials, such as NV centers in diamond, where decoherence can be influenced by factors like strain, temperature, and electromagnetic fields. [0011] Control Mechanism Variations: Feedback-Based Control: Implementing real-time feedback loops where qubit decoherence is monitored, and noise parameters are dynamically adjusted to achieve a target decoherence profile. Pulse Shaping: Employing sophisticated pulse-shaping techniques using arbitrary waveform generators to tailor the temporal profile of the noise, enabling fine-grained control over decoherence dynamics. [0012] Hybrid System Variations: The quantum processor can be integrated with different types of classical processors, such as: Neuromorphic Chips: Leveraging the unique capabilities of neuromorphic architectures for processing the analog signals generated by the partially decohered qubits. FPGAs: Utilizing the reconfigurability of FPGAs to implement custom control algorithms and data processing pipelines. Broader Impact [0013] Quantum Simulation: Controlled non-Markovian decoherence can be used to simulate complex quantum systems in various fields: Materials Science: Simulating the behavior of materials under different environmental conditions to aid in the discovery of new materials with desired properties. Drug Discovery: Modeling molecular interactions to accelerate drug development by enabling more accurate and efficient simulations of drug-target binding. [0014] Quantum Machine Learning: Noise-Enhanced Learning: Introducing controlled decoherence as a form of “noise” during training can potentially enhance the performance of quantum machine learning algorithms by facilitating exploration of the solution space. Robustness to Noise: The ability to control decoherence can make quantum machine learning models more robust to environmental noise, a crucial step towards practical applications. [0015] Fundamental Quantum Physics Research: The system provides a powerful platform for studying the fundamental nature of decoherence and the transition between quantum and classical behavior, potentially leading to new insights into the foundations of quantum mechanics. [0016] Quantum Cryptography: Enhanced Security Protocols: Controlled decoherence could potentially be used to create new quantum key distribution protocols that are more resistant to eavesdropping attacks, leveraging the unique properties of non-Markovian decoherence for enhanced security. [7] Claims A quantum computing system comprising: a quantum processor comprising a plurality of qubits; a decoherence control module configured to induce controlled, non-Markovian decoherence in the qubits via engineered noise channels characterized by: a frequency spectrum with a defined, non-uniform profile designed to overlap with specific energy level transitions of the qubits, wherein the profile’s deviation from uniformity is greater than 50% over a bandwidth of at least 1 GHz; and a temporal profile dynamically adjustable with a time resolution of better than 1 nanosecond to modify qubit decoherence rates and pathways, wherein the temporal profile is adjustable in amplitude, duration, and shape; and a hybrid interface configured to couple the quantum processor to a classical processor, wherein the hybrid interface is further configured to: generate analog signals from partially decohered states of the qubits, the analog signals having a bandwidth of at least 1 kHz and a signal-to-noise ratio greater than 20 dB; and t transmit control signals from the classical processor to the decoherence control module to adjust the frequency spectrum and temporal profile of the engineered noise channels in a closed-loop feedback system. A method for performing a quantum computation, the method comprising: encoding information in a plurality of qubits; applying engineered noise with a controlled frequency spectrum and temporal profile to selectively modify decoherence rates and pathways of individual or groups of qubits, wherein the frequency spectrum has a defined, non-uniform profile designed to overlap with specific energy level transitions of the qubits, and the temporal profile is dynamically adjustable with a time resolution of better than 1 nanosecond, and wherein the applied engineered noise induces non-Markovian decoherence in the qubits, to steer the system towards a desired solution state, wherein the desired solution state corresponds to a specific quantum state or set of quantum states that represent the solution to a computational problem encoded in the initial state of the qubits; and reading out the computational result from the resulting state of the qubits. The system of claim 1, wherein the engineered noise channels comprise one or more of: terahertz-frequency pulses generated by Josephson junction arrays with adjustable pulse duration (1-100 picoseconds), frequency (0.1-10 THz), and amplitude, synchronized to qubit resonance frequencies with sub-picosecond precision; phononic lattice vibrations in piezoelectric substrates (e.g., AlN, ZnO) with lithographically defined interdigitated transducers to generate surface acoustic waves in the frequency range of 0.1-5 GHz; tailored laser pulses wi