Quantum-based node addressing: A new era for multi-chip interconnects

As 5G, 6G, and future networks continue to expand, a significant challenge arises regarding the efficiency of communication among devices within increasingly complex systems. With millions of connected devices, from IoT sensors to AI-driven applications, efficient management of device identifiers is critical for smooth data flow. Efficiently assigning and managing these identifiers across network nodes helps prevent data delays, ensuring that information reaches the right destination without interruption.

In 5G networks, ultra-low latency and high bandwidth are necessary to support critical applications like autonomous vehicles and smart cities, where even slight communication delays can disrupt service. As we advance to 6G and cutting-edge applications that rely on real-time data, high-speed data exchange between devices and systems will require new solutions for managing device identifiers. These solutions need to be adaptable and scalable to meet the high demands of next-generation telecom technologies.

A key innovation area lies in developing advanced schemes for managing network device addresses and identifiers. However, traditional methods can be resource-intensive and less adaptable, often relying on fixed, complex mathematical frameworks that are difficult to scale. This has made it challenging to meet the evolving needs of interconnected systems and maintain efficient communication as systems become increasingly sophisticated.

As multi-chip architectures become the norm—particularly with advancements in interconnect technologies like CXL and UCIe—the need for efficient, scalable address management becomes even more crucial. Effective address mapping within and across these chips is essential for achieving the speed and adaptability required in these high-performance systems.

Another key area of innovation lies in developing advanced addressing schemes powered by quantum computing. By integrating distributed quantum computing, these methods can dynamically adapt to changing configurations, boosting the efficiency and performance of memory and device addressing across interconnected systems.

This post explores how quantum computing offers new ways to tackle these address challenges. It introduces more flexible and dynamic approaches that align with industry standards like CXL and UCIe, paving the way for efficient and interconnected multi-chip networks of the future.

What is an addressing scheme?

In telecommunication networks, an addressing scheme refers to the system that assigns unique identifiers—such as IP addresses, memory addresses, or mobile phone numbers—to each device connected to the network. In environments focused on efficient data management, these addresses play a crucial role in device identification. Typically, a controller node oversees the assignment of these addresses, ensuring effective organization and operational efficiency throughout the network. Robustness in sharing these addresses is vital for maintaining trust within the computing infrastructure, particularly when it comes to optimized data management in 5G and future networks. An effective addressing scheme should prioritize essential features such as access control, isolation, and authentication to ensure seamless communication and maintain system integrity.

However, existing addressing schemes are often complex and can impede the efficient identification of network nodes. They rely heavily on intricate mathematical problems and certification processes, which can slow down operations and lead to potential delays in communication. This complexity can make it challenging to quickly and accurately manage device identifiers across the network, resulting in inefficiencies in data exchange.

The limitations of current addressing schemes underscore the need for a new approach to streamline information management. Emerging technologies like quantum computing offer innovative solutions that can enhance the efficiency of identifier management and improve overall system performance. Embracing quantum-based systems could transform the way devices are identified and communicate within networks, particularly as we progress into the 5G, 6G, and future generations, where efficient and reliable communication is crucial.

Adoption of quantum computing

Quantum computing leverages the principles of quantum mechanics to process information in ways that classical computers cannot. Unlike traditional bits, quantum bits (qubits) can exist in multiple states simultaneously, enabling vastly more powerful computations. This capability positions quantum computing as a transformative technology in fields requiring efficient data processing, making it essential for future networks.

However, current quantum technologies face significant challenges in scaling up the number of qubits on a single quantum chip. Despite advancements in quantum hardware, using technologies like ion traps, superconductors, and quantum dots, achieving a high qubit count is still challenging due to built-in technological limitations.

Recognizing this bottleneck, academic researchers and industry experts are increasingly advocating for a paradigm shift toward distributed quantum computing (DQC). Instead of relying solely on individual quantum processors with limited qubit capacities, DQC proposes to interconnect multiple modular and compact quantum computers via a quantum network infrastructure.

DQC enables quantum networking, where interconnected quantum processors are essential for performing distributed quantum computations. By connecting multiple quantum processors through such a network, DQC enables quantum operations to be performed across various locations, enabling the sharing and processing of quantum information between different nodes. 

In the context of telecommunications networks, integrating DQC holds significant promise. One potential application is in efficient address sharing, where the unique capabilities of quantum computing can enhance the management and processing of identifiers across the network. This approach illustrates how quantum algorithms can be integrated with compute clusters within telecom networks to optimize performance and improve operational efficiency.

The building blocks of an efficient addressing scheme

Our solution introduces an addressing scheme inspired by quantum phase estimation (QPE), utilizing the manipulation of quantum states across qubits distributed in a network. By leveraging quantum principles, this approach efficiently generates and shares unique addresses between network nodes, enhancing efficiency in distributed systems.

The proposed solution takes as input a network system that is composed of a controller node and several computing nodes, as well as the connections between them. The nodes in a network could have QPUs and other components, such as CPU, memory, and GPU. The controller is the central node that assigns addresses, allocates distributed tasks, sends execution instructions to nodes, and keeps track of data and communication qubits used during the execution. The other nodes are basic computing units, responsible for executing tasks or quantum circuits assigned to them by the controller. Figure 1 illustrates a topology example of a controller node with N computing nodes.

Figure 1: Topology example: A controller node connected with N computing nodes

Each computing node that requires an address, possesses address qubits on its quantum processor that can be denoted as Q1. These address qubits are initiated in a superposition state. The scheme takes the qubit on the quantum processor denoted as Q2 of the controller node into state |1⟩ which is an eigenstate of the rotation operator used for addressing. Next, it encodes the address in a composite system of Q1 and Q2 qubits (it applies a controlled-rotation operator iteratively where qubit on Q1 is controlled qubit and those on Q2 are target qubits). Then, it uses the phase kick-back method to encode an address on Q1 qubits. To decode the address, it applies the inverse quantum Fourier transform to Q1 and then measures Q1 in the computational basis to get the secret address. 

The output consists of the optimized addresses assigned to each computing node within the network system. An address is shared between the controller and a specific computing node within the network and is not accessible to a third party.  Additionally, whenever a new node joins the network system, our proposed solution efficiently assigns it a new identity. In the end, the controller has a list of addresses for all the computing nodes, and each node knows its own address.

How to generate optimized addresses?

In the following sections, we outline the steps followed by our solution to establish the optimized addressing scheme, as shown in Figure 2, along with the corresponding circuit implementation in Figure 3. 

Figure 2: Our proposed solution generates the secret addressing scheme.

The steps are structured as follows:

Step 1: Deploying new compute nodes: New compute nodes are deployed in the network to enhance its computational capacity.

Step 2: Establishing connections: The central node, known as the controller node, establishes quantum and classical connections with each newly deployed compute node that joins the network.

Step 3: Initiating entanglement: The controller establishes quantum entanglement with the new node to begin the address assignment process for the new node.

Step 4: State vector selection and address assignment: The controller selects its state vector to coincide with the eigenstate of a rotation operator of its choice.

The binary representation of the rotation angle of this operator serves as the designated address.  Utilizing the entanglement, the operator is applied to the combined system comprising the new node and the controller. The controller's state is unchanged due to its status as an eigenstate of the operator, so the eigenvalue becomes part of the new node.

Step 5: Qubit measurement by the new node: The new node measures all associated qubits in the Fourier basis to extract the encoded address.

Step 6: Updating the addressing scheme: Upon successfully decoding the address, the controller integrates it into the existing addressing scheme and updates the IP address table.

These steps, from 1 to 6, are iterated for each node within the network.

Quantum circuit implementation

Figure 3 shows the quantum circuit implementation of our proposed solution. It allows the controller to assign an address to each node, one at a time, using the concept of phase kick-back. The solution puts all the addressing qubits (referred to as Q1) on each computing node into equal superposition. At the same time, a qubit on the controller node (referred to as Q2) is set to the state |1⟩, acting as an eigenstate of the parameterized Rz rotation in Bloch sphere, for securely sharing secret addresses between the nodes. 

Figure 3: A circuit diagram of our proposed QPE-inspired solution to obtain the address for the compute node-n.

Next, it encodes the address in a composite system of Q1 and Q2 qubits (it applies parameterized controlled-Rz iteratively, where Q1 is the controlled qubit and Q2 is the target qubit) using shared entanglement. We choose the |1⟩ state on the controller side as it is an eigenstate of the Rz operator. The parameter ϕ of parameterized controlled-Rz rotation is then the eigenvalue and can take any value between 0 to 2π, which gives us an infinite number of possible addresses. 

Our addressing scheme solution uses the concept of phase kick-back to encode the address on Q1 qubits of the computing node. To decode the shared address at the computing node’s end, the computing node measures Q1 qubits in the Fourier basis, which is done by applying the inverse quantum Fourier transform (QFT†) on Q1 before measuring them.  

Note that the number of possible addresses is constrained by the number of addressing bits (m) for ϕ utilized in the implementation of the addressing scheme. Therefore, the controller must select random addresses from a finite set of 2m options, which streamlines and accelerates the selection of random addresses.

Evaluating the solution

We present the evaluation results of our proposed solution on a DQC simulator:

• Setup: We implemented the proposed solution in a DQC setup in a simulator. In this setup, a controller node encodes an address onto the qubits of a computing node through a parameterized controlled rotation operation. The computing node then measures its qubits to retrieve the address, which remains known only between the controller and the specific computing node.
• Metrics: To evaluate the performance of our solution, we are measuring (1) elapsed simulation time and (2) success probabilities of intended address distribution with or without noises. Here, the elapsed simulation time refers to the time spent simulating our addressing scheme. The success probability, or accuracy, is the proportion of simulation iterations in which the correct intended address is obtained, relative to the total number of iterations.
• Results: The preliminary results demonstrate the scalability and efficiency of our proposed addressing scheme. For a 3-bit address, it takes 34.5 ns to assign, which increases to 230 ns for a 20-bit address, confirming scalability as the address length grows. Under mild memory noises, such as depolarizing and dephasing (probability 0.01), the success rate for distributing 3-bit addresses is around 80 percent as shown in Figure 4. With T1 and T2 times from IBM's 133-qubit system, the success rate remains above 80 percent, showcasing the robustness of the scheme against memory noise.

 

Summary

In this blog post, we propose an innovative and efficient addressing scheme designed to enhance reliability and performance by leveraging the QPE algorithm and the unique capabilities of quantum computing, such as superposition and non-classical operations. Our solution efficiently manages the allocation of addresses between the controller node and computing nodes, tackling scalability and performance challenges in 5G, 6G, and beyond. By aligning with industry standards like CXL and UCIe, our innovative quantum computing-based approach ensures seamless communication across multi-chip systems. We have also rigorously evaluated our solution to assess its performance under varying conditions, including scenarios with and without noise models.

In the future, we plan to expand our research to real modular systems and assess the efficiency of the proposed solution in managing channel delays and noise in long-distance modular systems.

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